LLM Engineering courses

• Understand Transformers, attention mechanisms, tokens, embeddings, context windows, and the core architectural foundations that enable modern large language models.
• Explore how large language models are pretrained on large-scale data, instruction-tuned for downstream tasks, and aligned to better follow human intentions and application requirements.
• Compare open and proprietary model families according to their capabilities, deployment options, accessibility, customization possibilities, and engineering constraints.
• Understand hallucinations, reasoning limitations, context constraints, latency, inference cost, and other technical trade-offs that influence the selection and use of LLMs in real applications.

• Design system prompts, instructions, few-shot examples, constraints, and structured output requirements that guide LLMs toward more reliable and predictable behavior.
• Build and manage context using conversation history, documents, user information, application state, retrieved knowledge, and other relevant runtime information.
• Apply prompt templates, task decomposition, dynamic context construction, and reusable prompting patterns to support maintainable LLM application development.
• Evaluate and iteratively optimize prompts and context strategies for accuracy, consistency, robustness, token efficiency, operational cost, and long-term maintainability.

• Integrate large language models into software applications through APIs, SDKs, and model-serving interfaces while understanding the responsibilities of the surrounding application layer.
• Implement streaming responses, structured outputs, tool calling, conversation state, model configuration, and robust error-handling patterns.
• Design model-routing, fallback, retry, timeout, and caching strategies that improve application resilience and enable appropriate use of different models.
• Build reliable LLM application layers while monitoring and controlling latency, token consumption, rate limits, availability, and operational costs.

• Understand embeddings and how semantic vector representations enable similarity-based retrieval beyond traditional keyword matching.
• Build document ingestion, cleaning, chunking, embedding, indexing, and metadata pipelines that prepare organizational knowledge for semantic retrieval.
• Implement semantic and hybrid search using vector databases, metadata filtering, lexical retrieval, and reranking techniques to improve result relevance.
• Evaluate retrieval quality using appropriate test queries and metrics, and optimize search pipelines for downstream LLM and knowledge-powered applications.

• Design end-to-end Retrieval-Augmented Generation architectures that connect large language models with external, current, private, or enterprise knowledge sources.
• Implement advanced retrieval techniques including query transformation, filtering, reranking, multi-stage retrieval, and context construction strategies.
• Build grounded generation workflows that support citations, source attribution, evidence traceability, and transparent connections between answers and retrieved information.
• Diagnose retrieval and generation failures systematically and improve answer relevance, groundedness, faithfulness, completeness, and factual accuracy.

See also: RAG Systems courses →

• Understand when prompting, Retrieval-Augmented Generation, fine-tuning, or combinations of these approaches are appropriate for different LLM application requirements.
• Prepare, clean, curate, structure, and validate high-quality datasets for model adaptation, domain specialization, and instruction tuning.
• Explore supervised fine-tuning and parameter-efficient adaptation techniques such as LoRA and QLoRA, including their practical benefits and engineering considerations.
• Evaluate adapted models against strong baseline systems to determine whether fine-tuning produces measurable improvements in quality, reliability, specialization, or operational performance.

• Build LLM-powered systems capable of selecting tools, interacting with APIs, retrieving information, maintaining state, and executing multi-step tasks.
• Explore planning, memory, state management, orchestration, reflection, and other mechanisms used to coordinate agentic workflows.
• Design single-agent and multi-agent architectures that distribute responsibilities and capabilities across complex application workflows.
• Implement boundaries, permissions, validation mechanisms, execution limits, monitoring, and human approval checkpoints to support reliable and controlled autonomous execution.

See also: AI Agents courses →

• Design representative evaluation datasets and metrics for measuring correctness, relevance, groundedness, hallucination, instruction following, and task completion.
• Combine automated evaluation, model-based evaluation, deterministic checks, and human assessment to obtain a more complete view of LLM application quality.
• Trace prompts, retrieved context, tool calls, model outputs, latency, failures, token consumption, and other operational signals in production systems.
• Continuously optimize model selection, prompts, retrieval strategies, application logic, response quality, execution speed, reliability, and operational cost using evaluation evidence.

• Understand prompt injection, jailbreaks, sensitive-data leakage, insecure model outputs, unsafe tool use, adversarial manipulation, and other security risks affecting LLM applications.
• Implement input and output validation, authentication and authorization controls, content safeguards, permission boundaries, and secure tool-execution mechanisms.
• Address privacy, bias, explainability, responsible data handling, transparency, and other governance considerations throughout the LLM application lifecycle.
• Build layered security, monitoring, governance, and human-oversight mechanisms appropriate for enterprise-grade LLM systems and their associated risk levels.

See also: AI Security courses →

• Architect complete production systems that combine large language models, Retrieval-Augmented Generation, agents, databases, APIs, evaluation components, monitoring, and user-facing applications.
• Design for scalability, reliability, caching, concurrency, latency, operational cost, fault tolerance, and resilience to changes or outages across model providers.
• Implement deployment, versioning, testing, monitoring, feedback collection, incident analysis, and continuous-improvement pipelines for LLM-powered applications.
• Deliver an end-to-end production-ready LLM application using modern LLM engineering and LLMOps practices for maintainability, observability, security, quality, and sustainable operation.